Overview

Dataset statistics

Number of variables12
Number of observations3729
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory349.7 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Warnings

ftr_time is highly correlated with ftr_alt and 4 other fieldsHigh correlation
ftr_alt is highly correlated with ftr_time and 4 other fieldsHigh correlation
ftr_pres is highly correlated with ftr_time and 3 other fieldsHigh correlation
ftr_temp is highly correlated with ftr_time and 4 other fieldsHigh correlation
ftr_hum is highly correlated with ftr_LONHigh correlation
ftr_DP is highly correlated with ftr_time and 4 other fieldsHigh correlation
ftr_WF is highly correlated with ftr_VEFHigh correlation
ftr_VEF is highly correlated with ftr_WFHigh correlation
ftr_LON is highly correlated with ftr_time and 4 other fieldsHigh correlation
ftr_time has unique values Unique
ftr_alt has unique values Unique

Reproduction

Analysis started2021-06-18 14:52:14.321465
Analysis finished2021-06-18 14:55:04.356445
Duration2 minutes and 50.03 seconds
Software versionpandas-profiling v2.12.0
Download configurationconfig.yaml

Variables

ftr_time
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct3729
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1979.554304
Minimum0
Maximum4229
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2021-06-18T14:55:05.531594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile191.4
Q1943
median1974
Q32978
95-th percentile3854.6
Maximum4229
Range4229
Interquartile range (IQR)2035

Descriptive statistics

Standard deviation1179.957404
Coefficient of variation (CV)0.5960722581
Kurtosis-1.184143764
Mean1979.554304
Median Absolute Deviation (MAD)1018
Skewness0.0653680396
Sum7381758
Variance1392299.476
MonotonicityStrictly increasing
2021-06-18T14:55:07.056307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
5971
 
< 0.1%
6211
 
< 0.1%
26681
 
< 0.1%
6171
 
< 0.1%
26641
 
< 0.1%
6131
 
< 0.1%
26601
 
< 0.1%
6091
 
< 0.1%
26561
 
< 0.1%
Other values (3719)3719
99.7%
ValueCountFrequency (%)
01
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
42291
< 0.1%
42261
< 0.1%
42251
< 0.1%
42241
< 0.1%
42231
< 0.1%

ftr_alt
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct3729
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9822.146897
Minimum164.1
Maximum21566.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2021-06-18T14:55:09.013560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum164.1
5-th percentile1096.132
Q14319.45
median9590.95
Q315001.9
95-th percentile19554.044
Maximum21566.76
Range21402.66
Interquartile range (IQR)10682.45

Descriptive statistics

Standard deviation6068.135542
Coefficient of variation (CV)0.6178013428
Kurtosis-1.228255086
Mean9822.146897
Median Absolute Deviation (MAD)5336.12
Skewness0.138932928
Sum36626785.78
Variance36822268.96
MonotonicityStrictly increasing
2021-06-18T14:55:11.039919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14013.431
 
< 0.1%
7636.681
 
< 0.1%
3497.471
 
< 0.1%
8897.341
 
< 0.1%
9451.291
 
< 0.1%
18096.061
 
< 0.1%
12317.011
 
< 0.1%
15024.981
 
< 0.1%
10584.691
 
< 0.1%
3994.761
 
< 0.1%
Other values (3719)3719
99.7%
ValueCountFrequency (%)
164.11
< 0.1%
174.781
< 0.1%
181.641
< 0.1%
184.851
< 0.1%
190.041
< 0.1%
ValueCountFrequency (%)
21566.761
< 0.1%
21550.311
< 0.1%
21544.531
< 0.1%
21539.451
< 0.1%
21533.111
< 0.1%

ftr_pres
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3496
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean382.6783052
Minimum43.1
Maximum995.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2021-06-18T14:55:12.277049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum43.1
5-th percentile60.34
Q1131.4
median304.2
Q3609.4
95-th percentile895.18
Maximum995.9
Range952.8
Interquartile range (IQR)478

Descriptive statistics

Standard deviation279.2458784
Coefficient of variation (CV)0.7297144223
Kurtosis-0.9918627232
Mean382.6783052
Median Absolute Deviation (MAD)207.8
Skewness0.5566674227
Sum1427007.4
Variance77978.26059
MonotonicityDecreasing
2021-06-18T14:55:13.850402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.93
 
0.1%
47.83
 
0.1%
57.23
 
0.1%
463
 
0.1%
50.23
 
0.1%
47.43
 
0.1%
47.23
 
0.1%
43.33
 
0.1%
58.73
 
0.1%
62.93
 
0.1%
Other values (3486)3699
99.2%
ValueCountFrequency (%)
43.11
 
< 0.1%
43.21
 
< 0.1%
43.33
0.1%
43.52
0.1%
43.72
0.1%
ValueCountFrequency (%)
995.91
< 0.1%
994.81
< 0.1%
9941
< 0.1%
993.71
< 0.1%
993.11
< 0.1%

ftr_temp
Real number (ℝ)

HIGH CORRELATION

Distinct2689
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31.12988737
Minimum-80.05
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative2729
Negative (%)73.2%
Memory size29.3 KiB
2021-06-18T14:55:15.136479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-80.05
5-th percentile-76.5
Q1-67.81
median-31.09
Q31.15
95-th percentile19.408
Maximum23.5
Range103.55
Interquartile range (IQR)68.96

Descriptive statistics

Standard deviation34.76340925
Coefficient of variation (CV)-1.116721331
Kurtosis-1.539961208
Mean-31.12988737
Median Absolute Deviation (MAD)35.85
Skewness0.08719578841
Sum-116083.35
Variance1208.494623
MonotonicityNot monotonic
2021-06-18T14:55:16.435238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.988
 
0.2%
14.398
 
0.2%
18.097
 
0.2%
13.687
 
0.2%
-67.657
 
0.2%
14.416
 
0.2%
14.596
 
0.2%
17.616
 
0.2%
15.566
 
0.2%
-0.646
 
0.2%
Other values (2679)3662
98.2%
ValueCountFrequency (%)
-80.051
 
< 0.1%
-801
 
< 0.1%
-79.971
 
< 0.1%
-79.882
0.1%
-79.853
0.1%
ValueCountFrequency (%)
23.51
< 0.1%
23.441
< 0.1%
23.381
< 0.1%
23.351
< 0.1%
23.322
0.1%

ftr_hum
Real number (ℝ≥0)

HIGH CORRELATION

Distinct841
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.10404934
Minimum5.1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2021-06-18T14:55:17.852495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile11.9
Q129.2
median69.1
Q383.7
95-th percentile98.8
Maximum100
Range94.9
Interquartile range (IQR)54.5

Descriptive statistics

Standard deviation29.88342357
Coefficient of variation (CV)0.4971948463
Kurtosis-1.279880987
Mean60.10404934
Median Absolute Deviation (MAD)24
Skewness-0.352246917
Sum224128
Variance893.0190045
MonotonicityNot monotonic
2021-06-18T14:55:19.148948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10090
 
2.4%
19.937
 
1.0%
19.833
 
0.9%
2031
 
0.8%
98.228
 
0.8%
20.125
 
0.7%
14.625
 
0.7%
98.720
 
0.5%
9819
 
0.5%
14.719
 
0.5%
Other values (831)3402
91.2%
ValueCountFrequency (%)
5.11
 
< 0.1%
5.24
0.1%
5.36
0.2%
5.42
 
0.1%
5.54
0.1%
ValueCountFrequency (%)
10090
2.4%
99.910
 
0.3%
99.87
 
0.2%
99.710
 
0.3%
99.66
 
0.2%

ftr_DP
Real number (ℝ)

HIGH CORRELATION

Distinct1003
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-36.50329847
Minimum-89.3
Maximum22.8
Zeros2
Zeros (%)0.1%
Negative2735
Negative (%)73.3%
Memory size29.3 KiB
2021-06-18T14:55:20.412136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-89.3
5-th percentile-86.2
Q1-78.7
median-35.1
Q30.9
95-th percentile16.8
Maximum22.8
Range112.1
Interquartile range (IQR)79.6

Descriptive statistics

Standard deviation37.59244346
Coefficient of variation (CV)-1.029836892
Kurtosis-1.553442418
Mean-36.50329847
Median Absolute Deviation (MAD)37.4
Skewness-0.02077239716
Sum-136120.8
Variance1413.191805
MonotonicityNot monotonic
2021-06-18T14:55:22.093880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.726
 
0.7%
-8222
 
0.6%
-81.921
 
0.6%
2.420
 
0.5%
1.619
 
0.5%
-81.819
 
0.5%
-85.518
 
0.5%
-85.618
 
0.5%
2.518
 
0.5%
2.818
 
0.5%
Other values (993)3530
94.7%
ValueCountFrequency (%)
-89.31
 
< 0.1%
-89.22
 
0.1%
-89.17
0.2%
-897
0.2%
-88.92
 
0.1%
ValueCountFrequency (%)
22.84
 
0.1%
22.710
0.3%
22.66
0.2%
22.56
0.2%
22.43
 
0.1%

ftr_WF
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1599
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.62392599
Minimum2
Maximum27.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2021-06-18T14:55:24.287466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.46
Q17.62
median10.71
Q316.78
95-th percentile25.886
Maximum27.82
Range25.82
Interquartile range (IQR)9.16

Descriptive statistics

Standard deviation6.390735287
Coefficient of variation (CV)0.5062399205
Kurtosis-0.4145815721
Mean12.62392599
Median Absolute Deviation (MAD)3.56
Skewness0.8461084955
Sum47074.62
Variance40.84149751
MonotonicityNot monotonic
2021-06-18T14:55:25.569322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.3516
 
0.4%
7.1113
 
0.3%
7.5712
 
0.3%
6.4611
 
0.3%
6.4811
 
0.3%
9.6511
 
0.3%
6.5311
 
0.3%
20.210
 
0.3%
8.6110
 
0.3%
7.6210
 
0.3%
Other values (1589)3614
96.9%
ValueCountFrequency (%)
22
0.1%
2.012
0.1%
2.051
< 0.1%
2.071
< 0.1%
2.11
< 0.1%
ValueCountFrequency (%)
27.822
0.1%
27.811
< 0.1%
27.81
< 0.1%
27.781
< 0.1%
27.771
< 0.1%

ftr_WD
Real number (ℝ≥0)

Distinct1119
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.8400644
Minimum51.4
Maximum241.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2021-06-18T14:55:27.056716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum51.4
5-th percentile55.5
Q186.2
median95.4
Q3115.7
95-th percentile226.6
Maximum241.4
Range190
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation48.35693956
Coefficient of variation (CV)0.4323758202
Kurtosis0.7862622877
Mean111.8400644
Median Absolute Deviation (MAD)12.3
Skewness1.362321189
Sum417051.6
Variance2338.393604
MonotonicityNot monotonic
2021-06-18T14:55:29.017590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.221
 
0.6%
9421
 
0.6%
94.721
 
0.6%
54.820
 
0.5%
94.618
 
0.5%
92.718
 
0.5%
87.117
 
0.5%
91.617
 
0.5%
96.117
 
0.5%
95.417
 
0.5%
Other values (1109)3542
95.0%
ValueCountFrequency (%)
51.42
0.1%
51.52
0.1%
51.62
0.1%
51.72
0.1%
51.81
< 0.1%
ValueCountFrequency (%)
241.41
< 0.1%
241.32
0.1%
241.11
< 0.1%
240.92
0.1%
240.81
< 0.1%

ftr_VEF
Real number (ℝ)

HIGH CORRELATION

Distinct357
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.41753821
Minimum-27.8
Maximum7.9
Zeros5
Zeros (%)0.1%
Negative3225
Negative (%)86.5%
Memory size29.3 KiB
2021-06-18T14:55:30.838717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-27.8
5-th percentile-25.5
Q1-14.1
median-10.2
Q3-6.3
95-th percentile5.3
Maximum7.9
Range35.7
Interquartile range (IQR)7.8

Descriptive statistics

Standard deviation8.549260393
Coefficient of variation (CV)-0.8206603343
Kurtosis-0.3954994455
Mean-10.41753821
Median Absolute Deviation (MAD)3.9
Skewness0.00333289412
Sum-38847
Variance73.08985327
MonotonicityNot monotonic
2021-06-18T14:55:32.754165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10.885
 
2.3%
-7.444
 
1.2%
-11.144
 
1.2%
-13.944
 
1.2%
-10.943
 
1.2%
-8.643
 
1.2%
-10.740
 
1.1%
-8.739
 
1.0%
-838
 
1.0%
-14.138
 
1.0%
Other values (347)3271
87.7%
ValueCountFrequency (%)
-27.84
0.1%
-27.76
0.2%
-27.64
0.1%
-27.55
0.1%
-27.45
0.1%
ValueCountFrequency (%)
7.93
 
0.1%
7.83
 
0.1%
7.76
0.2%
7.613
0.3%
7.52
 
0.1%

ftr_VNF
Real number (ℝ)

Distinct184
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8362295522
Minimum-10.9
Maximum7.5
Zeros27
Zeros (%)0.7%
Negative1266
Negative (%)34.0%
Memory size29.3 KiB
2021-06-18T14:55:34.257263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-10.9
5-th percentile-8.7
Q1-1.2
median1
Q34
95-th percentile6.8
Maximum7.5
Range18.4
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation4.229665295
Coefficient of variation (CV)5.058019396
Kurtosis0.3538876118
Mean0.8362295522
Median Absolute Deviation (MAD)2.7
Skewness-0.7680518726
Sum3118.3
Variance17.89006851
MonotonicityNot monotonic
2021-06-18T14:55:35.432685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.780
 
2.1%
0.673
 
2.0%
0.972
 
1.9%
0.471
 
1.9%
1.167
 
1.8%
0.564
 
1.7%
1.660
 
1.6%
5.460
 
1.6%
1.254
 
1.4%
0.853
 
1.4%
Other values (174)3075
82.5%
ValueCountFrequency (%)
-10.912
0.3%
-10.84
 
0.1%
-10.722
0.6%
-10.69
0.2%
-10.54
 
0.1%
ValueCountFrequency (%)
7.510
 
0.3%
7.422
0.6%
7.316
0.4%
7.221
0.6%
7.130
0.8%

ftr_LAT
Real number (ℝ≥0)

Distinct2636
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.024448209
Minimum8.000892
Maximum8.043898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2021-06-18T14:55:36.728616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8.000892
5-th percentile8.005005
Q18.011932
median8.023506
Q38.038214
95-th percentile8.043468
Maximum8.043898
Range0.043006
Interquartile range (IQR)0.026282

Descriptive statistics

Standard deviation0.01302979926
Coefficient of variation (CV)0.001623762646
Kurtosis-1.334399587
Mean8.024448209
Median Absolute Deviation (MAD)0.012634
Skewness-0.01566025267
Sum29923.16737
Variance0.0001697756687
MonotonicityNot monotonic
2021-06-18T14:55:37.917204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0381628
 
0.2%
8.0113318
 
0.2%
8.0195538
 
0.2%
8.0382038
 
0.2%
8.0437097
 
0.2%
8.0195937
 
0.2%
8.0437727
 
0.2%
8.0381577
 
0.2%
8.038227
 
0.2%
8.043726
 
0.2%
Other values (2626)3656
98.0%
ValueCountFrequency (%)
8.0008922
0.1%
8.0009141
< 0.1%
8.0009492
0.1%
8.0009551
< 0.1%
8.0011
< 0.1%
ValueCountFrequency (%)
8.0438981
< 0.1%
8.0438922
0.1%
8.0438751
< 0.1%
8.0438631
< 0.1%
8.0438581
< 0.1%

ftr_LON
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3537
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.297044363
Minimum2.01786
Maximum2.446152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.3 KiB
2021-06-18T14:55:39.179847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.01786
5-th percentile2.0679296
Q12.16087
median2.341759
Q32.42741
95-th percentile2.4455076
Maximum2.446152
Range0.428292
Interquartile range (IQR)0.26654

Descriptive statistics

Standard deviation0.1360069914
Coefficient of variation (CV)0.05920956232
Kurtosis-1.255356498
Mean2.297044363
Median Absolute Deviation (MAD)0.097248
Skewness-0.5009675136
Sum8565.678431
Variance0.01849790171
MonotonicityNot monotonic
2021-06-18T14:55:40.526621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.444926
 
0.2%
2.4458945
 
0.1%
2.446025
 
0.1%
2.4459285
 
0.1%
2.4459515
 
0.1%
2.4458775
 
0.1%
2.4458145
 
0.1%
2.4458194
 
0.1%
2.4454534
 
0.1%
2.4458544
 
0.1%
Other values (3527)3681
98.7%
ValueCountFrequency (%)
2.017861
< 0.1%
2.018451
< 0.1%
2.0186331
< 0.1%
2.0188171
< 0.1%
2.0189941
< 0.1%
ValueCountFrequency (%)
2.4461521
< 0.1%
2.4461461
< 0.1%
2.446141
< 0.1%
2.4461291
< 0.1%
2.4461232
0.1%

Interactions

2021-06-18T14:52:45.969340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:47.019521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:48.029658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:48.954757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:49.847866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:50.784614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:51.658889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:52.675628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:53.638921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:54.641585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:55.541486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:56.422483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:57.316501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:58.257121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:52:59.250060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:00.137869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:01.020374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:01.902949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:02.895191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:03.853903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:04.838237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:05.726627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:06.703173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:07.650279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:08.570022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:09.707317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:10.687327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:11.839626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:12.802581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:13.821383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:14.770056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:15.750050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:16.668173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:17.622208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:18.572769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:19.534247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:20.521364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:21.545051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:22.495778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:23.410471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:24.443814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:25.403585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:26.392969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:27.330784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:28.275193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:29.194028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:30.076028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:30.972474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:31.911725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:32.830096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:33.717259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:35.905001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:36.878180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:37.879734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:38.787210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:39.693430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:40.644440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:41.635319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:42.658823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:43.672505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:44.616887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:45.526694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:46.528242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:47.646129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:48.655259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:49.575828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:50.484736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:51.365184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:52.338262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:53.235716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:54.134064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:55.009224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:55.995785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:56.988848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:57.919637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:58.892622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:53:59.774878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:00.719805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:01.791065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:02.903969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:03.950132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:04.998987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:06.022628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:07.085271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:08.118525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:09.180948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:10.281357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:11.335207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:12.430496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:13.426768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:14.395082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:15.403280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:16.462952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:17.415838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:18.485796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:19.473651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:20.997022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:22.378172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:23.587956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:24.600050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:25.644559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:26.789120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:27.866350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:28.970689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:30.036097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:31.187683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:32.317499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:33.574999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:34.648897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:35.772344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:36.824189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:37.854624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:38.902787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:40.038312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:42.175315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:43.087897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:44.018438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:44.906610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:45.920147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:47.068392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:48.147157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:49.069346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:49.957522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:50.844342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:51.925788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:52.867557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:53.805039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:54.740395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:55.624830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:56.651987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:57.650305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-18T14:54:58.659609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-18T14:55:41.683788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-18T14:55:44.920604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-18T14:55:46.537906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-18T14:55:48.323057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-18T14:55:00.548615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-18T14:55:03.237453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ftr_timeftr_altftr_presftr_tempftr_humftr_DPftr_WFftr_WDftr_VEFftr_VNFftr_LATftr_LON
00164.10995.923.5090.021.82.00155.0-1.42.18.0008922.427891
12174.78994.823.4496.022.82.50146.5-1.42.18.0008922.427886
23181.64994.023.3896.122.72.44146.5-1.32.08.0009142.427897
34184.85993.723.3296.422.72.36147.4-1.32.08.0009552.427909
45190.04993.123.3296.622.72.26149.5-1.11.98.0010062.427932
56197.70992.223.2396.922.72.15152.4-1.01.98.0010002.427943
67203.97991.523.2996.722.72.07155.7-0.91.98.0009492.427920
78210.88990.723.3596.522.82.01159.2-0.71.98.0009492.427851
89213.16990.723.2996.822.82.00163.0-0.61.98.0010062.427794
910216.51990.323.2997.122.82.01167.1-0.42.08.0011042.427771

Last rows

ftr_timeftr_altftr_presftr_tempftr_humftr_DPftr_WFftr_WDftr_VEFftr_VNFftr_LATftr_LON
3719421221475.0543.8-67.285.3-85.722.2176.7-21.6-5.18.0219712.021229
3720421321481.0243.7-67.175.3-85.722.2576.5-21.6-5.28.0219312.021051
3721421421485.6943.7-67.175.3-85.722.2876.4-21.7-5.28.0218902.020862
3722421821505.9543.5-66.975.3-85.522.3076.2-21.7-5.38.0216442.020009
3723421921512.2643.5-66.865.2-85.522.3276.0-21.7-5.48.0215982.019785
3724422321533.1143.3-66.955.3-85.522.3375.9-21.7-5.48.0213922.018994
3725422421539.4543.3-66.885.2-85.522.3475.9-21.7-5.58.0213352.018817
3726422521544.5343.3-67.025.2-85.622.3675.8-21.7-5.58.0212832.018633
3727422621550.3143.2-67.155.2-85.822.3775.8-21.7-5.58.0212142.018450
3728422921566.7643.1-66.955.1-85.722.3775.8-21.7-5.58.0209742.017860